Discovering biological connections between experimental conditions based on common patterns of differential gene expression.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:21951600", "kind": "science-record", "revision": 7, "content_hash": "sha256:48bb760c8b24e6a09b354bd7a7cc05bbe9f3d010dfe3511b63a45027af21db61", "created_at": "2026-11-01T15:40:02Z", "updated_at": "2026-11-01T21:58:08Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Discovering biological connections between experimental conditions based on common patterns of differential gene expression.", "doi": "10.1186/1471-2105-12-381", "pmid": "21951600", "authors": ["Adam C Gower", "Avrum Spira", "Marc E Lenburg"], "journal": "BMC bioinformatics", "year": 2011, "abstract": "BACKGROUND: Identifying similarities between patterns of differential gene expression provides an opportunity to identify similarities between the experimental and biological conditions that give rise to these gene expression alterations. The growing volume of gene expression data in open data repositories such as the NCBI Gene Expression Omnibus (GEO) presents an opportunity to identify these gene expression similarities on a large scale across a diverse collection of datasets. We have developed a fast, pattern-based computational approach, named openSESAME (Search of Expression Signatures Across Many Experiments), that identifies datasets enriched in samples that display coordinate differential expression of a query signature. Importantly, openSESAME performs this search without prior knowledge of the phenotypic or experimental groups in the datasets being searched. This allows openSESAME to identify perturbations of gene expression that are due to phenotypic attributes that may not have been described in the sample annotation included in the repository. : To demonstrate the utility of openSESAME, we used gene expression signatures of two biological perturbations to query a set of 75,164 human expression profiles that were generated using Affymetrix microarrays and deposited in GEO. The first query, using a signature of estradiol treatment, identified experiments in which estrogen signaling was perturbed and also identified differences in estrogen signaling between estrogen receptor-positive and -negative breast cancers. The second query, which used a signature of silencing of the transcription factor p63 (a key regulator of epidermal differentiation), identified datasets related to stratified squamous epithelia or epidermal diseases such as melanoma.\nCONCLUSIONS: openSESAME is a tool for leveraging the growing body of publicly available microarray data to discover relationships between different biological states based on common patterns of differential gene expression. These relationships may serve to generate hypotheses about the causes and consequences of specific patterns of observed differential gene expression.", "claims": [{"id": "claim-method", "statement": "A signature association (SA) score is calculated for each sample using a Wilcoxon rank-sum test to compare the normalized expression levels of the \"up\" and \"down\" genes.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:21951600", "locator": "sections[3] \"openSESAME Algorithm\" paragraph 4", "quote": "A signature association (SA) score is calculated for each sample using a Wilcoxon rank-sum test to compare the normalized expression levels of the \"up\" and \"down\" genes."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All publicly available CEL files from the Gene Expression Omnibus data repository [50] as of September 23, 2010 in which RNA had been profiled on an HG-U133 generation (HG-U133A, HG-U133B, HT-HG-U133A, HG-U133A 2.0, or HG-U133 Plus 2.0) Affymetrix GeneChip were downloaded.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:21951600", "locator": "sections[12] \"Gene Expression Omnibus (GEO) dataset acquisition and normalization\" paragraph 2", "quote": "All publicly available CEL files from the Gene Expression Omnibus data repository [50] as of September 23, 2010 in which RNA had been profiled on an HG-U133 generation (HG-U133A, HG-U133B, HT-HG-U133A, HG-U133A 2.0, or HG-U133 Plus 2.0) Affymetrix GeneChip were downloaded."}], "state": "complete"}, {"id": "claim-target-result", "statement": "openSESAME uses a Wilcoxon rank-sum-derived signature association score followed by Fisher’s exact or Kolmogorov–Smirnov testing to identify coordinate differential expression in GEO-sourced microarray profiles, achieving a ROC AUC of 0.981 for distinguishing E2-treated samples.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:21951600", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "To demonstrate the utility of openSESAME, we used gene expression signatures of two biological perturbations to query a set of 75,164 human expression profiles that were generated using Affymetrix microarrays and deposited in GEO."}, {"asset": "source:pmid:21951600", "locator": "sections[6] \"Performance of the openSESAME algorithm\" paragraph 2", "quote": "We calculated SA scores using the E2 treatment signature for each of these samples and found that the area under the Receiver Operating Characteristics (ROC) curve for distinguishing E2-treated from non-E2-treated samples was 0.981 (Additional File 5 orange curve)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "openSESAME uses a Wilcoxon rank-sum-derived signature association score followed by Fisher’s exact or Kolmogorov–Smirnov testing to identify coordinate differential expression in GEO-sourced microarray profiles, achieving a ROC AUC of 0.981 for distinguishing E2-treated samples.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:21951600"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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The first", "ng signature association (SA) scores between microarray samples and an expression signature For each", "E query was performed using the original 189-Affymetrix-probeset E2 treatment signature on the ranke"], "order_basis": "paper_text_order:1341", "id": "assay:1"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["or decreased > 2.5-fold by the treatment of MCF7 cells with 10 nM 17-β-estradiol (E2) for eit", "eatment signature in Connectivity Map (CMap) MCF7 dataset. An openSESAME query was performed u", "d changes from all instances of treatment of MCF7 cells in the CMap build 2.0 dataset. 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